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Area of Science:

  • Agricultural science
  • Food security research
  • Climate change adaptation

Background:

  • Reliable crop production estimates are vital for effective food security policies and interventions.
  • Enhancing food production on existing cropland requires accurate assessments of potential yield.
  • Current 'top-down' modeling approaches may provide unreliable estimates of agricultural potential.

Purpose of the Study:

  • To evaluate the performance of 'top-down' gridded frameworks against a 'bottom-up' approach for estimating crop production potential.
  • To highlight the uncertainties associated with 'top-down' methods in predicting food security outcomes.
  • To inform the development of more accurate methods for assessing global food production capacity.

Main Methods:

  • Comparison of 'top-down' frameworks (Global Agro-ecological Zones, Agricultural Model Intercomparison and Improvement Project) with a 'bottom-up' approach (Global Yield Gap Atlas).
  • Local estimation of extra production potential by the Global Yield Gap Atlas, followed by upscaling to larger spatial scales.
  • Case study analysis for sub-Saharan Africa to illustrate the impact of different estimation approaches on food security prognoses.

Main Results:

  • 'Top-down' frameworks produced alarmingly unlikely estimates, with potential production lower than current farm yields in some locations.
  • The choice of methodology significantly impacts predictions of future cereal self-sufficiency, as shown in the sub-Saharan Africa example.
  • Significant uncertainty exists in yield potential and yield gap assessments derived from 'top-down' approaches.

Conclusions:

  • 'Bottom-up' approaches, like the Global Yield Gap Atlas, offer more reliable estimates of crop production potential.
  • Relying solely on 'top-down' methods for food security foresight and research priority setting introduces high uncertainty.
  • Incorporating 'bottom-up' estimates is recommended to improve the accuracy of food security predictions and agricultural research planning.